This disclosure relates generally to single cell culture and analysis by microscopy and MALDI mass spectrometry.
The last several decades have seen a tremendous progress in the understanding of biological processes. An ever-growing number of assays, made available through highly-automated tools such as quantitative PCR instruments and high-throughput DNA sequencers, have allowed the analysis of molecular mechanisms of these processes in exquisite detail.
Despite these advances, research in many important fields, such as immunology and cancer biology, has made it increasingly clear that bulk measurements (i.e. on the level of cell populations and tissue homogenates) can mask characteristics of individual cells or subsets of cells that contribute significantly to biological processes, but may not be identical to the “population average” measured by these techniques. Such heterogeneity appears not only in genetically diverse populations (as in the case of many tumors, or T and B lymphocytes), but also at the clonal level, based on differences in epigenetic states of the cells and stochasticity in cellular signaling. In addition, interactions between individual players may not be resolved if only an “average” behavior is studied. As a result, traditional methods may draw a misleading picture of dynamic responses of cells to the given perturbations, necessitating development of technologies for single-cell analysis.
Important classes of measurements include characterization of genotype, proliferation, cell surface markers, secreted molecules and interactions between individual cells. Tracing these parameters for every cell may reveal not only new biology (such as specific pathways and interactions in the immune system), but also inform diagnosis and treatment (for example, based on known mutations in cancer). Although taking in vivo measurements would in many cases be preferable to preserve the natural state of cells and their microenvironments, it is often impossible or impractical. In vitro measurements are less restrictive, but may also pose limitations on the number and types of assays that may be applied. First, they should offer a means to isolate individual cells for subsequent interrogation. They should also be sensitive enough to reliably detect signals associated with each cell. The number of simultaneously detected signals (multiplexity) may become another limitation. Some measurements (such as genetic profiling) may be done as an end-point assay, while others (such as functional or phenotypic characteristics) may be repetitive due to their non-destructive nature. Another important aspect is the throughput of any assay, which poses a practical limit on how many measurements and on how many cells can be feasibly done in a given time; methods that process cells in parallel may be much more efficient than serial ones. No less important is the number of cells required for a particular method, which implies the kinds of samples that could be processed with it: while cell lines and animal models are an abundant source of material used in research, clinical samples often barely meet this criterion. In this regard, any method that preserves viability and identity of cells for subsequent analyses (i.e. being modular) may provide additional flexibility. Finally, fine control over individual cells and their measurements should be carefully balanced with the overall simplicity of the approach, as doing so may drastically affect the costs and labor spent on any analysis.
Optical tools have become a de-facto standard for the majority of measurements in biological research, both at the bulk and the single-cell levels. Indeed, high sensitivity and resolution (down to the single-molecule level) enabled by advanced light sources and detectors—lasers, photomultiplier tubes (PMTs), electron multiplying charge-coupled devices (EMCCDs) etc. (which in turn, resulted from advances in physical sciences and microfabrication)—coupled with the generally non-destructive nature of optical measurements (allowing repeated, time-course and modular experiments) and an ever-growing availability of fluorescent probes, provided a unique framework for studying biological processes not easily achieved with any other physical observations. However, optical measurements still suffer from reduced multiplexity due to the overlapping spectra of the dyes, the requirement for labels specific to target molecules, and oftentimes a necessity to amplify the analytes (such as nucleic acids). As such, a number of orthogonal methods that complement or substitute fluorescence measurements by relying on alternative physics, have been developed: Raman spectroscopy, mass spectrometry, methods using electric and magnetic fields (capillary electrophoresis (CE), dielectrophoresis (DEP), iso-dielectric separation (IDS)), as well as mechanical forces (acoustic or inertial focusing). All these techniques greatly expand the arsenal of tools available for single-cell analysis, allowing researchers to choose the best set of tools for each case.
While the majority of tools for bulk and even single-cell analysis has traditionally been developed at the “macro” scale, the advancement of technologies for fabrication of microstructures in recent years has enabled production of miniaturized systems, or “lab on a chip” (LoC) devices. These tools utilize the concept of confinement of cells or their lysates and associated reagents to a small volume, often on the order of nanoliters or less. They not only serve to isolate cells from each other, but also increase local concentrations of analytes to achieve higher sensitivity, reduce cost of consumables and time spent on each analysis (often by processing many cells or reactions in parallel or in fast iterations), as well as automate analytical pipelines and reduce potential for human error. Prominent examples include passive and actively actuated microfluidic traps, droplet encapsulation and microwell arrays. Passive devices trap one or a few cells in their structures and trace their growth or responses to stimuli with fluorescence microscopy; although efficient trapping is possible, it provides inhomogeneous shared environment dependent on the position of a cell in the stream. Actively actuated traps may offer better control over microenvironment and cross-contamination, but require the use of a complicated set of microfluidic valves and pumps that is limited by the number of connections and as such is not scalable to large numbers of cells (>103-104). Droplet-based devices encapsulate cells in small volumes of water-in-oil emulsions and provide room for combinatorial screening and sorting; although highly-controlled and efficient loading is possible, and cell viability may be preserved over extended time, controlling individual droplets still requires significant manipulations. In all cases above, a high degree of control, efficiency and throughput, guided by computational optimization based on hydrodynamics simulations, is achievable; however, it comes at a cost of an overall complexity of the analysis and limited potential for retrieval of individual cells of interest.
The most common choice of material for LoC devices has been poly(dimethylsiloxane) (PDMS). Its wide adoption in academic labs is based on a unique combination of properties: ease of replication based on soft lithography, high optical transparency and low autofluorescence compared to common plastics, versatility in surface modifications and elasticity allowing it to conformally seal against hard surfaces and be actuated, permeability to gases, relative inertness and biocompatibility, along with a few others. Even with all these advantages, however, PDMS is not an ideal material for some applications: its high permeability to small non-polar molecules and water vapor may severely affect measurements involving hydrophobic moieties and affect viability of cells sensitive to changes of osmolality in the media; its high elasticity precludes fabrication of microstructures with aspect ratios higher than 2:1 (e.g. tall posts) or lower than 0.2 (shallow channels or wells), and affects reliable registration for large-scale devices. Finally, and perhaps most importantly, PDMS is not readily amenable to large-scale replication: while manual handling in an academic lab is straightforward, it takes a substantial time to cure the polymer (1-2 h), and the overall process may leave residues on the mold. Industrial processes (such as injection molding), to the contrary, benefit from materials that can easily be processed and ejected in a matter of seconds, making large-scale production cost effective.
The World Health Organization (WHO) classifies over 125 types of brain tumors based on histopathological evaluation, assigning type, subtype and grade (I-IV) to each one of them. Gliomas, tumors of the tissue that support and protect neurons, account for 30% of them, with astrocytoma and oligodendroglioma being the most malignant forms, 70% grade III or higher. Glioblastoma (GBM) is astrocytoma grade IV, with a median survival of 12 to 15 months. Meningiomas account for another 34.7% of all brain tumors. GBM is infamous for being one of the most heterogeneous tumors in existence, as defined by numerous phenotypic and genetic factors. Tumor sustainability and drug resistance are direct results of this heterogeneity, wherein tumor survives due to hyper-adaptive subpopulations. Identification of these tumors based on histopathology and magnetic resonance imaging (MRI) can be challenging, and decisions on surgical resection are in many cases too aggressive if the boundary between the tumor and the normal tissue is unclear. This results in postoperative morbidity, i.e. undesired neurological deficits.
To help with identification and characterization of tumor heterogeneity, new modalities for tumor imaging are needed. More complete knowledge about the tumors could inform clinical decisions, including surgical decisions and optimization of adjuvant treatment, and further enhance our understanding of tumor biology. Whereas conventional tools for resolving heterogeneity revolve around fluorescence microscopy at the proteomic and genomic levels, mass spectrometric techniques for identification and quantification of complex ({tilde under (>)}100 Da) molecules have also become available in the last two decades. Desorption electrospray ionization (DESI) mass spectrometry has recently been shown to accurately identify tumor type, grade and cellularity based on lipid imaging of tissue sections from stereotactic biopsies with a high degree of accuracy in near real time (i.e., during an operation). Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has been used for a proteomic-based prognosis in a similar context. These advances demonstrate a clear clinical impact associated with such technologies.
While conventional methods rely on expensive probes (such as antibodies and DNA tags) that are specific to the molecules of interest, mass-spectrometric techniques mentioned above allow for direct imaging of small and large molecules without the need for any labeling. In case of small molecules (such as lipids and metabolites), highly specific probes may not even exist. Additionally, this enables work in “discovery mode”, where mass spectrum signals can be analyzed with little or no a priori knowledge of what particular molecules to look for, which would not be possible with methods that rely on labeling of target analytes. Little or no sample preparation also reduces the time and effort required for MSI compared to probe-based analyses.
Thus, a need exists for systems and methods that allow a user to culture and interrogate a population of cells on a cell-by-cell basis, using fluorescence microscopy and MALDI-MSI.